Retrieval-augmented generation

RAG enables LLMs to access new information without retraining

Retrieval-augmented generation (RAG) allows large language models (LLMs) to retrieve and incorporate new information from external sources, enhancing their ability to provide up-to-date responses. This technique supplements the LLM's pre-existing training data with domain-specific and/or updated information, enabling them to access internal company data or authoritative sources for generating responses.

Example

A chatbot using RAG can access and provide the latest financial reports from a company's database, even if it wasn't trained on that specific data.

Remember this

RAG's ability to access new information without retraining is crucial for maintaining the relevance and accuracy of LLM-generated content in rapidly changing domains.

Text adapted from Wikipedia, licensed under CC BY-SA 4.0.

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